Nonlinear model predictive control for omnidirectional robot motion planning and tracking

Abstract

We present a nonlinear model predictive control algorithm for online motion planning and tracking of an omnidirectional autonomous robot. The formalism is based on the minimization of a control Hamiltonian related to the cost function. This minimization is constrained by a nonlinear plant model. The algorithm considers point obstacles and uses a potential field to penalize proximity to those obstacles. A simple and demonstrative example simulation is presented.